# Graph Neural Network based Channel Tracking for Massive MIMO Networks

arXiv: https://arxiv.org/abs/2004.08738 · HTML: https://mlsanity.com/paper/2004.08738/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization](https://mlsanity.com/paper/2207.05364/) (2022)
- [Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility](https://mlsanity.com/paper/2102.09785/) (2021)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Learning Autonomy in Management of Wireless Random Networks](https://mlsanity.com/paper/2106.07984/) (2021)
- [Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO](https://mlsanity.com/paper/2108.03941/) (2021)
- [Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint](https://mlsanity.com/paper/2405.05802/) (2024)
- [Denoised Recommendation Model with Collaborative Signal Decoupling](https://mlsanity.com/paper/2511.04237/) (2025)

## Abstract (arXiv)

In this paper, we resort to the graph neural network (GNN) and propose the new channel tracking method for the massive multiple-input multiple-output networks under the high mobility scenario. We first utilize a small number of pilots to achieve the initial channel estimation. Then, we represent the obtained channel data in the form of graphs and describe the channel spatial correlation by the weights along the edges of the graph. Furthermore, we introduce the computation steps of the main unit for the GNN and design a GNN-based channel tracking framework, which includes an encoder, a core network and a decoder. Simulation results corroborate that our proposed GNN-based scheme can achieve better performance than the works with feedforward neural network.